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FACIAL EXPRESSION PROCESSING IN AUTISM SPECTRUM DISORDER AS A FUNCTION OF ALEXITHYMIA: AN EYE MOVEMENT STUDY
Title
FACIAL EXPRESSION PROCESSING IN AUTISM SPECTRUM DISORDER AS A FUNCTION OF ALEXITHYMIA: AN EYE MOVEMENT STUDY.
Creator
Escobar, Brian, Hong, Sang Wook, Florida Atlantic University, Department of Psychology, Charles E. Schmidt College of Science
Abstract/Description
The perception and interpretation of faces provides individuals with a wealth of knowledge that enables them to navigate their social environments more successfully. Prior research has hypothesized that the decreased facial expression recognition (FER) abilities observed in autism spectrum disorder (ASD) may be better explained by comorbid alexithymia, the alexithymia hypothesis. The present study sought to further examine the alexithymia hypothesis by collecting data from 59 participants and...
Show moreThe perception and interpretation of faces provides individuals with a wealth of knowledge that enables them to navigate their social environments more successfully. Prior research has hypothesized that the decreased facial expression recognition (FER) abilities observed in autism spectrum disorder (ASD) may be better explained by comorbid alexithymia, the alexithymia hypothesis. The present study sought to further examine the alexithymia hypothesis by collecting data from 59 participants and examining FER performance and eye movement patterns for ASD and neurotypical (NT) individuals while controlling for alexithymia severity. Eye movement-related differences and similarities were examined via eye tracking in conjunction with statistical and machine-learning-based pattern classification analysis. In multiple different classifying conditions, where the classifier was fed 1,718 scanpath images (either at spatial, spatial-temporal, or spatial temporal-ordinal levels) for high-alexithymic ASD, high-alexithymicvi NT, low-alexithymic ASD, and low-alexithymic NT, we could accurately decode significantly above chance level. Additionally, in the cross-decoding analysis where the classifier was fed 1,718 scanpath images for high- and low alexithymic ASD individuals and tested on high- and low-alexithymic NT individuals, results showed that classification accuracy was significantly above chance level when using spatial images of eye movement patterns. Regarding FER performance results, we found that ASD and NT groups performed similarly, but at lower intensities of expressions, ASD individuals performed significantly worse than NT individuals. Together, these findings suggest that there may be eye-movement related differences between ASD and NT individuals, which may interact with alexithymia traits.
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Date Issued
2023
PURL
http://purl.flvc.org/fau/fd/FA00014358
Subject Headings
Autism Spectrum Disorder, Machine learning, Facial expression, Alexithymia, Eye tracking
Format
Document (PDF)
TEMPORAL DYNAMICS OF NEGATIVE AND POSITIVE FACIAL EXPRESSION PROCESSING
Title
TEMPORAL DYNAMICS OF NEGATIVE AND POSITIVE FACIAL EXPRESSION PROCESSING.
Creator
Escobar, Brian E., Hong, Sang Wook, Florida Atlantic University, Department of Psychology, Charles E. Schmidt College of Science
Abstract/Description
The perception and interpretation of faces provides individuals with a wealth of knowledge that enables them to navigate their social environments more successfully. The present study examined the temporal dynamics of valence information from emotional facial expressions using electroencephalogram (EEG) in conjunction with multi-variate pattern analysis (MVPA). In multiple different classifying conditions, it was demonstrated that when decoding for a positively- vs. a negatively- vs. a...
Show moreThe perception and interpretation of faces provides individuals with a wealth of knowledge that enables them to navigate their social environments more successfully. The present study examined the temporal dynamics of valence information from emotional facial expressions using electroencephalogram (EEG) in conjunction with multi-variate pattern analysis (MVPA). In multiple different classifying conditions, it was demonstrated that when decoding for a positively- vs. a negatively- vs. a neutrally-valenced expression, above chance level decoding accuracy occurs sooner when compared to instances of decoding for a negatively- vs. a negatively- vs. a neutrally-valenced expression. Additionally, results showed that classification accuracy as measured by percentage of correct responses was higher in the classification condition with the positively-valenced expression versus the one with two negatively-valenced expressions. Together, these finding suggest that neural processing of facial expression may occur hierarchical manner, in that categorization between between-valence (positive vs. negative) facial expressions precedes categorization among within-valence.
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Date Issued
2021
PURL
http://purl.flvc.org/fau/fd/FA00013862
Subject Headings
Facial expression, Electroencephalography, Facial expression--Research
Format
Document (PDF)